A simple LeNet based classifier used to classify German Traffic Signs Dataset.

The model for training the dataset involves some adaptations from the research paper titled Traffic Sign Recognition with Multi-Scale Convolutional Networks by Pierre Sermanet and Yann LeCun.

1. Traffic_Sign_Classifier.ipynb

• The file consists of all the required cells to load, preprocess, train, validate and test the data.

2. net.jpg

• It is a reference image for the architecture of the classifier model deployed in the project.

3. /extra_images

• Consists of some random images downloaded from internet to test the classifier

• Anaconda

• Download the appropriate Anaconda version as per your local system.
• Download the German Traffic Signs Dataset. You can also use any other appropriate dataset that can be compatible with the model.
• Using Jupyter Notebook open the .ipynb file
• Execute the cells.
• Enjoy the "World of Deep Learning" ;)

• Included in the repo